Project Summary Recent studies based on single-cell analysis have underscored a far greater diversity of cells within a tissue ecosystem than suspected. Subsets of cells have frequently been found to be critical in the onset and progression of a wide range of systemic diseases. The advent of next-generation sequencing techniques makes single-cell genomics and single-cell transcriptomics broadly accessible. However, no equivalent platform is available yet for investigating single-cell functional proteomics. It has been well known for decades that functional proteins are essential for most cellular processes, and they are widely used as phenotyping markers and drug targets. The current state-of-the-art single-cell protein profiling tools only measure dozens of proteins per cell, which is not enough to cover the wide spectrum of the functional proteome. We have recently innovated a multiplex in situ tagging (MIST) technique based on a compact monolayer of DNA-encoded microparticles through successive rounds of labeling and imaging. This technique can easily achieve a multiplexity of tens of thousands using a common fluorescence microscope and a simple procedure that can be executed in a typical biological laboratory setting. Our preliminary data show that the MIST array covers an area ~10,000 times smaller than the prevailing microarray, without compromising high sensitivity at ~100 molecules per cell. The MIST array will be integrated with our portable stand-sit microchip that can handle primary cell samples and make proteins in single cells available for analysis. The three aims we propose include: (1) Create and optimize an integrated system combining MIST with stand-sit microchip for analysis of functional proteins in >10,000 single cells; (2) Screen a library of 1,000 barcoded DNAs, improve multiplexity and sensitivity of the MIST array, and quantify 150 signaling proteins and surface markers in mouse primary peripheral blood mononuclear cells; and (3) Develop a framework for data analysis to visualize high- dimensional data, classify cell subtypes by both functions and phenotypes, and determine signaling networks of each subtype. To the end, we will have a robust, inexpensive, and user-friendly single-cell functional proteomic tool that can routinely measure ~100-1,000 proteins per cell with a high sensitivity and a high throughput. This project will enable the implementation of single-cell functional proteomics as a common tool in the broader biomedical community. The application of this technology will generate influential results as single- cell transcriptomics does to the biomedical sciences.